Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Giha Lee"'
Publikováno v:
International Soil and Water Conservation Research, Vol 12, Iss 4, Pp 868-884 (2024)
Accurate soil organic carbon storage (SOCS) estimation is crucial for sustaining ecosystem health and mitigating climate change impacts. This study investigated the accuracy and variability of SOCS predictions, focusing on the role of pedotransfer fu
Externí odkaz:
https://doaj.org/article/b31d244c61fe4677adb44429427945bd
Publikováno v:
Frontiers in Environmental Science, Vol 12 (2024)
Landslide susceptibility mapping (LSM) is essential for determining risk regions and guiding mitigation strategies. Machine learning (ML) techniques have been broadly utilized, but the uncertainty and interpretability of these models have not been we
Externí odkaz:
https://doaj.org/article/92298d057c3843948190e995d14338f4
Publikováno v:
Water, Vol 16, Iss 14, p 1945 (2024)
This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boostin
Externí odkaz:
https://doaj.org/article/0bc375eeebbc437384097b26f14f9a31
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
Landslides are a major natural hazard that can significantly damage infrastructure and cause loss of life. In South Korea, the current landslide susceptibility mapping (LSM) approach is mainly based on statistical techniques (logistic regression (LR)
Externí odkaz:
https://doaj.org/article/7644444193d942919b7bb0d435310297
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 48, Iss , Pp 101475- (2023)
Study region: South Korea is situated in the northeastern region of Asia Study focus:: Recent technological developments have enabled multi-source precipitation products (MSPs), including satellite-based and model-based, to be useful data sources for
Externí odkaz:
https://doaj.org/article/a8411e8e352a4c2c8f865a50565a5344
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 46, Iss , Pp 101328- (2023)
Study region: The Ca River basin is located in the North Central Coast area of Vietnam Study focus: This study aims to develop a deep learning framework that is both effective and straightforward in order to forecast water levels in the Ca River basi
Externí odkaz:
https://doaj.org/article/7263cb6f07914197a05a01acfa87fe7b
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 116, Iss , Pp 103177- (2023)
Despite satellite-based precipitation products (SPPs) providing a worldwide span with a high spatial and temporal resolution, their efficiency in disaster risk forecasting, hydrological, and watershed management remains a challenge due to the signifi
Externí odkaz:
https://doaj.org/article/d63cdb2bae9e4fdd80ce9f85958f83d1
Publikováno v:
Frontiers in Environmental Science, Vol 10 (2022)
When raindrops collide with the topsoil surface, they cause soil detachment, which can be estimated by measuring the kinetic energy (KE) of the raindrops. Considering their direct measurements on terrestrial surfaces are challenging, empirical equati
Externí odkaz:
https://doaj.org/article/411af21b1a6546c6b410d41df729e043
Publikováno v:
IEEE Access, Vol 9, Pp 71805-71820 (2021)
Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to the sup
Externí odkaz:
https://doaj.org/article/9e85309437e74a9e9ff047694645bcd4
Publikováno v:
Progress in Earth and Planetary Science, Vol 7, Iss 1, Pp 1-16 (2020)
Abstract Climate change currently affects the resilience and aquatic ecosystem. Climate change alters rainfall patterns which have a great impact on river flow. Annual flooding is an important hydrological characteristic of the Mekong River Basin (MR
Externí odkaz:
https://doaj.org/article/3239837e245f4dcda6fabbb7fdb59f9e